System for network traffic analysis and anomaly detection.

AI & Machine Learning: Detection and Classification of Network Traffic Anomalies based on IoT23 Dataset

ISCXFlowMeter is an Ethernet traffic flow generator and analyzer for anomaly detection which has been used in different network security datasets such as ISCX VPN dataset (ISCXVPN2016) and ISCX Tor dataset (ISCXTor2016).

Anomaly detection in network traffic and event logs using deep learning (w/ Pytorch)

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It was first identified in December 2019 in Wuhan, China, and has since spread globally, resulting in an ongoing pandemic. Long Short Term Memories(LSTMs) can solve numerous tasks not solvable by previous learning algorithms for recurrent neural networks (RNNs). LSTM is applicable to tasks such as unsegmented, connected handwriting recognition, speech recognition and anomaly detection in network traffic or IDS's (intrusion detection systems). LSTMs can also be efficiently applied for time-series predictions. In this project, its shows a four stacked LSTM network for early prediction new Coronavirus dissease infections in some of the mentioned affected countries (India, USA, Czech Republic and Russia) , which is based on real world data sets which are analyzed using various perspectives like day-wise number of confirmed cases, number of Cured cases, death cases. This attempt has been done to help the concerned authorities to get some early insights into the probable devastation likely to be effected by the deadly pandemic.

Detection of network traffic anomalies using unsupervised machine learning

This project aims to detect the anomalies in Web-Traffic using a C-LSTM architecture.

A Network Anomaly Detection system that leverages machine learning to monitor and identify unusual activities in network traffic in real-time. This project is designed to enhance network security by providing early detection of potential threats and anomalies.

Replication files for arXiv:1803.10769 Network Traffic Anomaly Detection Using Recurrent Neural Networks

Intrusion Detection Systems (IDSs) and Intrusion Prevention Systems (IPSs) are the most important defense tools against the sophisticated and ever-growing network attacks. Due to the lack of reliable test and validation datasets, anomaly-based intrusion detection approaches are suffering from consistent and accurate performance evolutions. Our evaluations of the existing eleven datasets since 1998 show that most are out of date and unreliable. Some of these datasets suffer from the lack of traffic diversity and volumes, some do not cover the variety of known attacks, while others anonymize packet payload data, which cannot reflect the current trends. Some are also lacking feature set and metadata. CICIDS2017 dataset contains benign and the most up-to-date common attacks, which resembles the true real-world data (PCAPs). It also includes the results of the network traffic analysis using CICFlowMeter with labeled flows based on the time stamp, source, and destination IPs, source and destination ports, protocols and attack (CSV files). Also available is the extracted features definition. Generating realistic background traffic was our top priority in building this dataset. We have used our proposed B-Profile system (Sharafaldin, et al. 2016) to profile the abstract behavior of human interactions and generates naturalistic benign background traffic. For this dataset, we built the abstract behaviour of 25 users based on the HTTP, HTTPS, FTP, SSH, and email protocols. The data capturing period started at 9 a.m., Monday, July 3, 2017 and ended at 5 p.m. on Friday July 7, 2017, for a total of 5 days. Monday is the normal day and only includes the benign traffic. The implemented attacks include Brute Force FTP, Brute Force SSH, DoS, Heartbleed, Web Attack, Infiltration, Botnet and DDoS. They have been executed both morning and afternoon on Tuesday, Wednesday, Thursday and Friday.

Purposed a network traffic classification and prediction model based on CNN, TCN and Attention mechanism.

Anomaly Detection in Network Traffic using different clustering algorithm.

convGRU based autoencoder for unsupervised & spatial-temporal anomaly detection in computer network (PCAP) traffic.

This project implements a cybersecurity system for detecting anomalies and intrusions in network traffic. It utilizes machine learning models, network monitoring tools, and intrusion detection systems to monitor and respond to security threats in real-time.

AEIDS is a prototype of anomaly-based intrusion detection system which works by remembering the pattern of legitimate network traffic using Autoencoder.

Anomaly Detection using PCA and BiGAN

This project integrates Explainable AI (XAI) techniques for anomaly detection in encrypted network traffic using ML Algorithms. We employ SHAP (SHapley Additive Explanations) to interpret model decisions and enhance transparency in detecting malicious activities. The system is designed to identify suspicious patterns in encrypted traffic.

A research & development project to create and deploy a Network-based Intrusion Detection System (IDS) to detect intruders on a distributed system. That is, it detects and classify threatening or anomalous network traffic as opposed to safe traffic and usage. The project runs on a real-time, distributed cluster on Apache Storm which processes incoming network packets, and uses our novel algorithms and Machine Learning to detect intruders. It uses supervised Machine Learning classifiers such as decision trees, ensemble decision trees, support vector machines, etc. as well as being built with the principles of anomaly-based Intrusion Detection Systems.

A project from EECS6414M of Winter 2020 at York University

Replication files for arXiv:1805.03735 Sequence Aggregation Rules for Anomaly Detection in Computer Network Traffic

A network intrusion detection system that monitors bidirectional network traffic from various locations and reports statistical anomalies to a central decision making server

unsupervised&semi-supervised Anomaly Detection methods for Network Traffic

An ensemble based machine learning for anomaly detection from the network traffic.

Anomaly detection in network traffic using unsupervised k-NN, Deep AutoEncoder and Isolation Forest

Accurate-Cyber-Defense-Advanced-Network-Monitoring-Bot is a cutting-edge, performance-oriented cybersecurity tool designed for real-time network surveillance, traffic analysis, anomaly detection, and threat alerting.

A machine learning project for anomaly detection and attack classification using the NF-CSE-CIC-IDS2018-v2 dataset, which contains over 18 million NetFlow records with 43 flow features. The project builds and evaluates models to identify and classify network intrusions from benign traffic.

Network Traffic Monitor and Analysis for anomalies detection and auto scaling infrastructure

Python Script that sniffs network traffic on a Apache server and use anomaly detection methods to detect DDoS attack.

An Unsupervised Deep Learning Model for Early Network Traffic Anomaly Detection

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